Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. …”
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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources by M. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed

“…In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. …”
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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…However, integrating these modalities poses challenges due to sensor heterogeneity and variability in placement. This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…In addition, the proposed method exhibits robustness in handling the inherent complexities and variabilities in polymer extrusion processes, thus offering a reliable solution for ensuring product quality and process efficiency. …”
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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…This study investigates the potential of combining ultrahigh-sensitivity Talbot-Lau interferometry with Convolutional Neural Networks (CNNs) to enhance breast tumor segmentation from scattering images. …”
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Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging by Chiara Manini, Markus Hüllebrand, Lars Walczak, Sarah Nordmeyer, Lina Jarmatz, Titus Kuehne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Jennifer Erley, Sebastian Kelle, Peter Bannas, Ralf Felix Trauzeddel, Jeanette Schulz-Menger, Anja Hennemuth

“…However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. …”
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Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…The proposed framework employs the latest attention and convolution-based encoders, such as ConvFormer, CAFormer, Swin Transformer, and ConvNextV2, in precisely identifying crop rows across varied and challenging agricultural environments. …”
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Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network by Stefano A. Bini, MD, Nicholas Gillian, PhD, Thomas A. Peterson, PhD, Richard B. Souza, PhD, PT, Brooke Schultz, MS, ACE-CPT, Wojciech Mormul, MS, Marek K. Cichoń, MS, Agnieszka Barbara Szczotka, MS, Ivan Poupyrev, PhD

“…Model performance was assessed using mean absolute error. Results: The convolutional neural network models exhibited high accuracy in replicating motion capture-derived kinematic variables. …”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…Abstract This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). …”
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Digital three-stage recursive-separable image processing filter with variable sizes of scanning multielement aperture by A. V. Kamenskiy, T. M. Akaeva, D. A. Grebenshchikova

“…The aim of the work is to develop a type of recursively separable digital filter with variable sizes of a scanning multielement aperture which allows the number of computational operations to be reduced while maintaining the efficiency of filtering input data (images).Methods. …”
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Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning by ZHANG Huiyun, ZUO Fangjun, LI Hang, YU Xi

“…To address the problem that it is difficult to label variable working condition gearbox fault samples and the significant data distribution discrepancies in practical engineering, which result in reduced accuracy of fault diagnosis models, a semi-supervised gearbox fault diagnosis method based on masked contrastive learning is proposed. …”
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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…This study proposes a multimodal bearing fault classification approach that relies on vibration and motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The method fuses features from multiple signals to enhance the accuracy of fault detection. …”
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Fault Diagnosis Based On Improved Information Entropy And 1dcnn For Marine Turbocharger Rotor With Variable Speed by Hu Lei, Hu Haoran, Hu Nao, Liu Luyuan, Dong Fei, Yang Jianguo, Zhong Jiahong

“…In addition, the uncertainty in the feature parameters used for diagnosis under variable rotational speeds leads to low accuracy in fault identification. …”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…Two methods toward generalization are proposed and compared: design-variable multilayer perceptron (DV-MLP) and design-variable hypernetworks (DVH). …”
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FAULT DIAGNOSIS OF GEARBOX UNDER VARIABLE WORKING CONDITION BASED ON WEIGHTED SUBDOMAIN ADAPTIVE ADVERSARIAL NETWORK by ZHANG Huiyun, ZUO Fangjun, YU Xi, YANG Ting

“…In practical engineering, gearboxes are subject to complex and variable operating environments, which hinder the ability of a single vibration signal to accurately and effectively represent fault information under different working conditions. …”
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Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition by Shyamala Devi M, Sundaravadivazhagan Balasubaramanian, Balasubramaniam S, Mohd Asif Shah

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Multi-Factor Deep Learning Model for Sea Surface Temperature Forecasting by Yuting Yang, Kin-Man Lam, Junyu Dong, Yakun Ju

“…To address these challenges, we propose a multi-sensor SST prediction model that integrates Long Short-Term Memory (LSTM) networks, convolutional neural networks (CNNs), and an attention mechanism to directly incorporate physical variables such as temperature, salinity, density, and current velocity. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. …”
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AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols by Umesh Kumar Lilhore, R. Sunder, Sarita Simaiya, Majed Alsafyani, M. D. Monish Khan, Roobaea Alroobaea, Hamed Alsufyani, Abdullah M. Baqasah

“…Traditional methods of tumor segmentation, often manual and labour-intensive, are prone to inconsistencies and inter-observer variability. Recently, deep learning models, particularly Convolutional Neural Networks (CNNs), have shown great promise in automating this process. …”
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Deep Learning for Cardiovascular Disease Detection by Shivan H. Hussein, Najdavan A. Kako

“…This work investigates the role and contribution of deep learning, especially Fully Convolutional Networks (FCNs) and Convolutional Neural Networks (CNNs), toward the improvement of accuracy and automation in cardiac MRI analysis. …”
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